1. Field of the Invention
The present invention generally relates to a multi-antenna communication system, and in particular, to an apparatus and method of generating a Log-Likelihood Ratio (LLR) of high reliability in a communication system using a spatial multiplexing scheme.
2. Description of the Related Art
Recently, with rapid growth of the wireless mobile communication market, various multimedia services in the wireless environment are becoming more heavily demanded. In particular, mass transmission data and rapid data delivery are progressing. Thus, an urgent task is to find a method of efficiently using limited frequencies. To respond to this, a new transmission technique using a multi-antenna is desired. By way of example of the new transmission technique, a Multiple Input Multiple Output (MIMO) system using a multi-antenna is being used.
The MIMO technique, which uses a multi-antenna at the transmitter and the receiver respectively, can increase the channel transmission capacity in proportion to the number of the antennas without additional frequencies or transmit power allocation, comparing to a system using a single antenna. Thus, in recent years, active research is being conducted on the MIMO technique.
The Multi-antenna techniques are divided largely to a spatial diversity scheme which improves the transmission reliability by acquiring a diversity gain corresponding to the product of the numbers of transmit and receive antennas, a Spatial Multiplexing (SM) scheme which increases the data rate by transmitting a plurality of signal streams at the same time, and a combination scheme of the spatial diversity and the SM.
When transmitters send different data streams using an SM scheme of the multi-antenna techniques, interference occurs between the data transmitted simultaneously. Hence, the receiver detects a signal using a Maximum Likelihood (ML) receiver by taking account of the influence of the interference signal, or detects the signal after rejecting the interference. The interference cancellation schemes include a Zero Forcing scheme, a Minimum Mean Square Error (MMSE) scheme, and so forth. In a general SM scheme, there is the trade-off between the receiver performance and the computational complexity of the receiver. Thus, active research is conducted on a reception process which can achieve performance approximate to an ML receiver with a low computational complexity of the receiver.
In the mean time, it is known that decoding by providing a soft decision value to a channel decoder is beneficial in terms of the performance, rather than providing a hard decision value of encoded bits. A input soft decision value of the decoder, which is an estimated value of modulated symbols transmitted in the channel, uses a Log-Likelihood Ratio (LLR) value. Accordingly, a receiver of an SM scheme uses a process which generates an optimum LLR from a corresponding reception process, besides a low-complexity reception process.
Conventional signal detection methods using the SM scheme include the ML, Successive Interference Cancellation (SIC), and Vertical Bell Labs Layered Space-Time (V-BLAST), and so forth.
To briefly explain the processes, a system model is defined. The system of the interest is a model including NT-ary transmit antennas and NR-ary receive antennas as shown in FIG. 1. When expressing a signal to transmit on each transmit antenna as xm, a receive signal y at the receiver can be expressed by Equation (1) below. It is assumed that the signal xm to transmit on the transmit antenna is an M-QAM signal. The number of the encoded bits that can be transmitted at a time is NT×log2(M).y=Hx+n  (1)
In Equation (1), y is a receive signal vector, x is the transmit symbol vector, and H is a channel coefficient matrix generated between the transmit antenna and the receive antenna, which is defined by Equation (2) below. n denotes an ambient Gaussian noise vector.
                                                        x              =                                                [                                                                          ⁢                                                                                                                                          x                            1                                                    ,                                                      x                            2                                                    ,                                                      x                            3                                                    ,                          …                          ⁢                                                                                                          ,                                                      x                                                          N                              T                                                                                                                                                            ⁢                                                                          ]                                T                                                                                        y              =                                                [                                                                          ⁢                                                                                                                                          y                            1                                                    ,                                                      y                            2                                                    ,                                                      y                            3                                                    ,                          …                          ⁢                                                                                                          ,                                                      y                                                          N                              R                                                                                                                                                            ⁢                                                                          ]                                T                                                                                        H              =                              [                                                                                                    h                        11                                                                                                            h                        12                                                                                    ⋯                                                                                      h                                                  1                          ⁢                                                      N                            T                                                                                                                                                                                                  h                        21                                                                                                            h                        22                                                                                    ⋯                                                                                      h                                                  2                          ⁢                                                      N                            Y                                                                                                                                                                          ⋮                                                              ⋮                                                              ⋯                                                              ⋮                                                                                                                          h                                                                              N                            R                                                    ⁢                          1                                                                                                                                    h                                                                              N                            R                                                    ⁢                          2                                                                                                            ⋯                                                                                      h                                                                              N                            R                                                    ⁢                                                      N                            T                                                                                                                                              ]                                                                        (        2        )            
In Equation (2), the channel coefficient matrix H is NR×NT matrix. The element hij corresponding to the i-th line and the j-th column denotes the channel response between the j-th transmit antenna and the i-th receive antenna.
Signal detection methods using the SM scheme are arranged as follows.
The ML scheme selects a symbol vector having the shortest direct distance by computing the Euclidean distance, as defined below in Equation (3), with respect to all symbol vectors in the constellation. In other words, the ML scheme, which measures the distance between y and Hx, determines a symbol vector having the shortest distance as a symbol vector with the highest similarity, that is, with the minimum error. However, it is hard to practically realize the ML scheme because the complexity increases by raising the length of the codeword to the power of the number of the transmit antennas as shown in MNT(M−ary,|c|=M).
                              x          ^                =                              arg            x                    ⁢          min          ⁢                                                                  y                -                Hx                                                    F            2                                              (        3        )            
The SIC scheme cancels the interference of the received signal by reconstructing the values of the hard decision at the previous step. However, if the hard decision values of the previous step suffer error, the SIC scheme aggravates the error in the next step. Thus, in every step, the reliability of the hard decision values deteriorates.
Accordingly, the SIC scheme needs to take account of error propagation which is the factor of the performance degradation. Specifically, since the decoding is performed in the order of the transmit antenna index regardless of the channel status during interference cancellation, interference cancellation is carried out without removing the transmit antenna of the great signal intensity. As a result, the performance of the transmit antenna signal with the weak signal intensity is not considerably enhanced. A V-BLAST process addresses this problem and shows the better performance than an existing SIC scheme by canceling interference in an order of transmit antennas having the greater signal intensity.
The Modified ML (MML) scheme, by ML-decoding the symbol vectors transmittable on the other transmit antennas, excluding a signal transmitted with an arbitrary transmit antenna, can determine the one signal through the simple slicing operation Q( ) as shown below in Equation (4). The MML scheme exhibits the performance similar to the ML scheme and its computational complexity increases by raising to the power of the number of the transmit antennas minus 1. That is, the ML scheme computes the Euclidean distance with respect to MNT-ary transmit vectors, whereas the MML computes the Euclidean distance with respect to MNT−1-ary transmit vectors and detects the signal of the rest symbol through the slicing operation as in Equation (4).
                              x          i                =                  Q          (                                                    h                i                H                                                                                                    h                    1                                                                    2                                      ⁢                          (                              y                -                                                      ∑                                          j                      ≠                      i                                                        ⁢                                                            h                      j                                        ⁢                                          x                      j                                                                                  )                                )                                    (        4        )            
Finally, the Recursive MML (RMML) scheme is suggested to far more mitigate the complexity of the MML. The RMML generates a plurality of subsystems by nulling the channel using Givens rotation and decides the MML in the minimum unit 2×2 channel. As such, the RMML scheme can mitigate the computational complexity with the performance similar to the ML by generating the subsystems (e.g., 3×3 and 2×2). Yet, the generation of the multiple subsystems implies the multiple candidate transmit vectors, which limit the complexity mitigation. In addition, since the decision is made in the 2×2 subsystem right away, the performance degradation arises like the SIC family.
Meanwhile, the LLR computation at the MIMO receiver differs depending on the reception processes. In the MIMO environment with the inter-signal interference, the reliability of the LLR value quite differs depending on the MIMO reception processes. The reliability of the LLR value directly affects the decoding performance of the decoder. In the ML receiver which is known as the optimum receiver amongst the various MIMO reception processes, the optimum LLR computation is expressed by Equation (5) below.
                                                                                          LLR                  optmum                                ⁡                                  (                                      b                    i                                    )                                            =                            ⁢                              log                ⁢                                                                  ⁢                                                      P                    ⁡                                          (                                                                        b                          i                                                =                                                                              +                            1                                                    |                          y                                                                    )                                                                            P                    ⁡                                          (                                                                        b                          i                                                =                                                                              -                            1                                                    |                          y                                                                    )                                                                                                                                              =                            ⁢                              log                ⁢                                                                  ⁢                                                      P                    ⁢                                          (                                                                        y                          |                                                      b                            i                                                                          =                                                  +                          1                                                                    )                                        ⁢                                          P                      ⁡                                              (                                                                              b                            i                                                    =                                                      +                            1                                                                          )                                                                                                                        P                      ⁡                                              (                                                                              y                            |                                                          b                              i                                                                                =                                                      -                            1                                                                          )                                                              ⁢                                          P                      ⁡                                              (                                                                              b                            i                                                    =                                                      -                            1                                                                          )                                                                                                                                                                    =                            ⁢                              log                ⁢                                                                  ⁢                                                      P                    ⁡                                          (                                                                        y                          |                                                      b                            i                                                                          =                                                  +                          1                                                                    )                                                                            P                    ⁡                                          (                                                                        y                          |                                                      b                            i                                                                          =                                                  -                          1                                                                    )                                                                                                                                              =                            ⁢                              log                ⁢                                                                                                                    ⁢                                                                  ∑                                                                              x                            +                                                    ∈                                                      C                            i                            +                                                                                              ⁢                                                                        P                          ⁡                                                      (                                                                                          y                                |                                x                                                            =                                                              x                                +                                                                                      )                                                                          ⁢                                                  P                          ⁡                                                      (                                                          x                              =                                                              x                                +                                                                                      )                                                                                                                                                                          ∑                                                                        x                          -                                                ∈                                                  C                          i                          -                                                                                      ⁢                                                                  P                        ⁡                                                  (                                                                                    y                              |                              x                                                        =                                                          x                              -                                                                                )                                                                    ⁢                                              P                        ⁡                                                  (                                                      x                            =                                                          x                              -                                                                                )                                                                                                                                                                                            =                            ⁢                              log                ⁢                                                                  ⁢                                                                            ∑                                                                        x                          +                                                ∈                                                  C                          i                          +                                                                                      ⁢                                                                  exp                        ⁡                                                  (                                                      -                                                                                                                                                                                                  y                                    -                                                                          Hx                                      +                                                                                                                                                                        2                                                                                            2                                ⁢                                                                  σ                                  2                                                                                                                                              )                                                                    ⁢                                                                        ∏                                                                                    b                              j                                                        ∈                                                          x                              +                                                                                                      ⁢                                                  P                          ⁡                                                      (                                                          b                              j                                                        )                                                                                                                                                                          ∑                                                                        x                          -                                                ∈                                                  C                          i                          -                                                                                      ⁢                                                                  exp                        ⁡                                                  (                                                      -                                                                                                                                                                                                  y                                    -                                                                          Hx                                      -                                                                                                                                                                        2                                                                                            2                                ⁢                                                                  σ                                  2                                                                                                                                              )                                                                    ⁢                                                                        ∏                                                                                    b                              j                                                        ∈                                                          x                              +                                                                                                      ⁢                                                  P                          ⁡                                                      (                                                          b                              j                                                        )                                                                                                                                                                                                                      ≈                            ⁢                              log                ⁢                                                                                                                    ⁢                                                                  max                                                                              x                            +                                                    ∈                                                      C                            i                            +                                                                                              ⁢                                              exp                        ⁡                                                  (                                                      -                                                                                                                                                                                                  y                                    -                                                                          Hx                                      +                                                                                                                                                                        2                                                                                            2                                ⁢                                                                  σ                                  2                                                                                                                                              )                                                                                                                                                max                                                                        x                          -                                                ∈                                                  C                          i                          -                                                                                      ⁢                                          exp                      ⁡                                              (                                                  -                                                                                                                                                                                      y                                  -                                                                      Hx                                    -                                                                                                                                                              2                                                                                      2                              ⁢                                                              σ                                2                                                                                                                                    )                                                                                                                                                                    =                            ⁢                                                1                                      2                    ⁢                                          σ                      2                                                                      ⁢                                  {                                                                                    min                                                                              x                            +                                                    ∈                                                      C                            i                            +                                                                                              ⁢                                                                                                                              y                            -                                                          Hx                              +                                                                                                                                2                                                              -                                                                  min                                                                              x                            -                                                    ∈                                                      C                            i                            -                                                                                              ⁢                                                                                                                              y                            -                                                          Hx                              -                                                                                                                                2                                                                              }                                                                                        (        5        )            
In Equation (5), bi denotes an i-th bit. P(bi=+1|y) denotes a probability of the i-th bit being ‘+1’ when the receive signal vector y is received, and P(bi=+1) denotes a probability of the i-th bit being ‘+1’. Ci+ denotes the set of x's when the i-th bit of the transmit signal vector x is ‘+1’, and Ci− denotes the set of x's when the i-th bit of the transmit signal vector x is ‘−1’. As one can see from Equation (5), since the LLR computation at the ML receiver has to calculate the Euclidean distance with respect to every combination of the transmit signal vector x, it is difficult to adopt it for the greater number of antennas or the high-level modulation scheme.
As discussed above, when using the SM scheme, what is demanded is a receiver structure which has low complexity and high reliability similar to the LLR of the ML.